Estimating the Epidemic Size of Superspreading Coronavirus Outbreaks in Real Time: Quantitative Study

Kitty Y. Lau, Jian Kang, Minah Park, Gabriel Leung, Joseph T. Wu, Kathy Leung

Research output: Contribution to journalArticlepeer-review


Background: Novel coronaviruses have emerged and caused major epidemics and pandemics in the past 2 decades, including SARS-CoV-1, MERS-CoV, and SARS-CoV-2, which led to the current COVID-19 pandemic. These coronaviruses are marked by their potential to produce disproportionally large transmission clusters from superspreading events (SSEs). As prompt action is crucial to contain and mitigate SSEs, real-time epidemic size estimation could characterize the transmission heterogeneity and inform timely implementation of control measures. Objective: This study aimed to estimate the epidemic size of SSEs to inform effective surveillance and rapid mitigation responses. Methods: We developed a statistical framework based on back-calculation to estimate the epidemic size of ongoing coronavirus SSEs. We first validated the framework in simulated scenarios with the epidemiological characteristics of SARS, MERS, and COVID-19 SSEs. As case studies, we retrospectively applied the framework to the Amoy Gardens SARS outbreak in Hong Kong in 2003, a series of nosocomial MERS outbreaks in South Korea in 2015, and 2 COVID-19 outbreaks originating from restaurants in Hong Kong in 2020. Results: The accuracy and precision of the estimation of epidemic size of SSEs improved with longer observation time; larger SSE size; and more accurate prior information about the epidemiological characteristics, such as the distribution of the incubation period and the distribution of the onset-to-confirmation delay. By retrospectively applying the framework, we found that the 95% credible interval of the estimates contained the true epidemic size after 37% of cases were reported in the Amoy Garden SARS SSE in Hong Kong, 41% to 62% of cases were observed in the 3 nosocomial MERS SSEs in South Korea, and 76% to 86% of cases were confirmed in the 2 COVID-19 SSEs in Hong Kong. Conclusions: Our framework can be readily integrated into coronavirus surveillance systems to enhance situation awareness of ongoing SSEs.

Original languageEnglish
JournalJMIR Public Health and Surveillance
Issue number1
StatePublished - 2024

Bibliographical note

Publisher Copyright:
©Kitty Y Lau, Jian Kang, Minah Park, Gabriel Leung, Joseph T Wu, Kathy Leung. Originally published in JMIR Public Health and Surveillance.


  • COVID-19
  • MERS
  • Middle East respiratory syndrome
  • SARS
  • SSE
  • coronavirus
  • coronavirus disease 2019
  • epidemic size
  • severe acute respiratory syndrome
  • superspreading event


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